



'''simple docstring'''






import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def  lowerCamelCase__			(		_A							):
  a		:   Any	  =	filter(lambda _A							: p.requires_grad					,     model.parameters()							)
  a		:   Union[str, Any]	  =	sum([np.prod(p.size()							) for p in model_parameters]							)
  return params


lowerCAmelCase:   Optional[int]					=		logging.getLogger(__name__)
def  lowerCamelCase__			(		_A					,     _A							):
  if metric == "rouge2":
    a		:   Optional[Any]	  =	'{val_avg_rouge2:.4f}-{step_count}'
  elif metric == "bleu":
    a		:   Optional[int]	  =	'{val_avg_bleu:.4f}-{step_count}'
  elif metric == "em":
    a		:   Tuple	  =	'{val_avg_em:.4f}-{step_count}'
  elif metric == "loss":
    a		:   Union[str, Any]	  =	'{val_avg_loss:.4f}-{step_count}'
  else:
    raise NotImplementedError(
        f"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
        ' function.'							)

  a		:   Optional[Any]	  =	ModelCheckpoint(
      dirpath=_A					,     filename=_A					,     monitor=f"""val_{metric}"""					,     mode='max'					,     save_top_k=1					,     every_n_epochs=1					,     )

  return checkpoint_callback
def  lowerCamelCase__			(		_A					,     _A							):
  return EarlyStopping(
      monitor=f"""val_{metric}"""					,     mode='min' if 'loss' in metric else 'max'					,     patience=_A					,     verbose=_A					,     )

class 				a__(   pl.Callback      ):







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    Optional[Any]		,     __snake_case					:    Optional[int]      ):
      a		:   str	  =	{F"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups      )}
      pl_module.logger.log_metrics(__snake_case      )







    @rank_zero_only
    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    pl.Trainer		,     __snake_case					:    pl.LightningModule		,     __snake_case					:    str		,     __snake_case					:    int=True      ):
      logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""      )
      a		:   Optional[Any]	  =	trainer.callback_metrics
      trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}      )
      # Log results
      a		:   List[str]	  =	Path(pl_module.hparams.output_dir      )
      if type_path == "test":
        a		:   Union[str, Any]	  =	od / 'test_results.txt'
        a		:   Union[str, Any]	  =	od / 'test_generations.txt'
      else:
        # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
        # If people want this it will be easy enough to add back.
        a		:   List[str]	  =	od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
        a		:   Optional[Any]	  =	od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
        results_file.parent.mkdir(exist_ok=__snake_case      )
        generations_file.parent.mkdir(exist_ok=__snake_case      )
      with open(__snake_case		,     'a+'      ) as writer:
        for key in sorted(__snake_case      ):
          if key in ["log", "progress_bar", "preds"]:
            continue
          a		:   Optional[Any]	  =	metrics[key]
          if isinstance(__snake_case		,     torch.Tensor      ):
            a		:   str	  =	val.item()
          a		:   Optional[int]	  =	F"""{key}: {val:.6f}\n"""
          writer.write(__snake_case      )

      if not save_generations:
        return

      if "preds" in metrics:
        a		:   Tuple	  =	'\n'.join(metrics['preds']      )
        generations_file.open('w+'      ).write(__snake_case      )







    @rank_zero_only
    def      lowercase_			( self					:    List[str]		,     __snake_case					:    int		,     __snake_case					:    str      ):
      try:
        a		:   Tuple	  =	pl_module.model.model.num_parameters()
      except AttributeError:
        a		:   Union[str, Any]	  =	pl_module.model.num_parameters()

      a		:   int	  =	count_trainable_parameters(__snake_case      )
      # mp stands for million parameters
      trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}      )







    @rank_zero_only
    def      lowercase_			( self					:    int		,     __snake_case					:    pl.Trainer		,     __snake_case					:    pl.LightningModule      ):
      save_json(pl_module.metrics		,     pl_module.metrics_save_path      )
      return self._write_logs(__snake_case		,     __snake_case		,     'test'      )







    @rank_zero_only
    def      lowercase_			( self					:    Any		,     __snake_case					:    pl.Trainer		,     __snake_case					:    int      ):
      save_json(pl_module.metrics		,     pl_module.metrics_save_path      )
      # Uncommenting this will save val generations
      # return self._write_logs(trainer, pl_module, "valid")




'''simple docstring'''






import os

try:
     from .build_directory_md import good_file_paths
except ImportError:
     from build_directory_md import good_file_paths  # type: ignore

lowerCAmelCase:   Optional[int]					=		list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

lowerCAmelCase:   Optional[Any]					=		[file for file in filepaths if file != file.lower()]
if upper_files:
     print(F"{len(upper_files)} files contain uppercase characters:")
     print('\n'.join(upper_files) + '\n')

lowerCAmelCase:   int					=		[file for file in filepaths if ' ' in file]
if space_files:
     print(F"{len(space_files)} files contain space characters:")
     print('\n'.join(space_files) + '\n')

lowerCAmelCase:   Union[str, Any]					=		[file for file in filepaths if '-' in file]
if hyphen_files:
     print(F"{len(hyphen_files)} files contain hyphen characters:")
     print('\n'.join(hyphen_files) + '\n')

lowerCAmelCase:   Union[str, Any]					=		[file for file in filepaths if os.sep not in file]
if nodir_files:
     print(F"{len(nodir_files)} files are not in a directory:")
     print('\n'.join(nodir_files) + '\n')

lowerCAmelCase:   int					=		len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
     import sys

     sys.exit(bad_files)